875 research outputs found
Gaussian Belief Propagation Based Multiuser Detection
In this work, we present a novel construction for solving the linear
multiuser detection problem using the Gaussian Belief Propagation algorithm.
Our algorithm yields an efficient, iterative and distributed implementation of
the MMSE detector. We compare our algorithm's performance to a recent result
and show an improved memory consumption, reduced computation steps and a
reduction in the number of sent messages. We prove that recent work by
Montanari et al. is an instance of our general algorithm, providing new
convergence results for both algorithms.Comment: 6 pages, 1 figures, appeared in the 2008 IEEE International Symposium
on Information Theory, Toronto, July 200
MIMO Networks: the Effects of Interference
Multiple-input/multiple-output (MIMO) systems promise enormous capacity
increase and are being considered as one of the key technologies for future
wireless networks. However, the decrease in capacity due to the presence of
interferers in MIMO networks is not well understood. In this paper, we develop
an analytical framework to characterize the capacity of MIMO communication
systems in the presence of multiple MIMO co-channel interferers and noise. We
consider the situation in which transmitters have no information about the
channel and all links undergo Rayleigh fading. We first generalize the known
determinant representation of hypergeometric functions with matrix arguments to
the case when the argument matrices have eigenvalues of arbitrary multiplicity.
This enables the derivation of the distribution of the eigenvalues of Gaussian
quadratic forms and Wishart matrices with arbitrary correlation, with
application to both single user and multiuser MIMO systems. In particular, we
derive the ergodic mutual information for MIMO systems in the presence of
multiple MIMO interferers. Our analysis is valid for any number of interferers,
each with arbitrary number of antennas having possibly unequal power levels.
This framework, therefore, accommodates the study of distributed MIMO systems
and accounts for different positions of the MIMO interferers.Comment: Submitted to IEEE Trans. on Info. Theor
High-Rate Space-Time Coded Large MIMO Systems: Low-Complexity Detection and Channel Estimation
In this paper, we present a low-complexity algorithm for detection in
high-rate, non-orthogonal space-time block coded (STBC) large-MIMO systems that
achieve high spectral efficiencies of the order of tens of bps/Hz. We also
present a training-based iterative detection/channel estimation scheme for such
large STBC MIMO systems. Our simulation results show that excellent bit error
rate and nearness-to-capacity performance are achieved by the proposed
multistage likelihood ascent search (M-LAS) detector in conjunction with the
proposed iterative detection/channel estimation scheme at low complexities. The
fact that we could show such good results for large STBCs like 16x16 and 32x32
STBCs from Cyclic Division Algebras (CDA) operating at spectral efficiencies in
excess of 20 bps/Hz (even after accounting for the overheads meant for pilot
based training for channel estimation and turbo coding) establishes the
effectiveness of the proposed detector and channel estimator. We decode perfect
codes of large dimensions using the proposed detector. With the feasibility of
such a low-complexity detection/channel estimation scheme, large-MIMO systems
with tens of antennas operating at several tens of bps/Hz spectral efficiencies
can become practical, enabling interesting high data rate wireless
applications.Comment: v3: Performance/complexity comparison of the proposed scheme with
other large-MIMO architectures/detectors has been added (Sec. IV-D). The
paper has been accepted for publication in IEEE Journal of Selected Topics in
Signal Processing (JSTSP): Spl. Iss. on Managing Complexity in Multiuser MIMO
Systems. v2: Section V on Channel Estimation is update
Optimal Linear Precoding Strategies for Wideband Non-Cooperative Systems based on Game Theory-Part I: Nash Equilibria
In this two-parts paper we propose a decentralized strategy, based on a
game-theoretic formulation, to find out the optimal precoding/multiplexing
matrices for a multipoint-to-multipoint communication system composed of a set
of wideband links sharing the same physical resources, i.e., time and
bandwidth. We assume, as optimality criterion, the achievement of a Nash
equilibrium and consider two alternative optimization problems: 1) the
competitive maximization of mutual information on each link, given constraints
on the transmit power and on the spectral mask imposed by the radio spectrum
regulatory bodies; and 2) the competitive maximization of the transmission
rate, using finite order constellations, under the same constraints as above,
plus a constraint on the average error probability. In Part I of the paper, we
start by showing that the solution set of both noncooperative games is always
nonempty and contains only pure strategies. Then, we prove that the optimal
precoding/multiplexing scheme for both games leads to a channel diagonalizing
structure, so that both matrix-valued problems can be recast in a simpler
unified vector power control game, with no performance penalty. Thus, we study
this simpler game and derive sufficient conditions ensuring the uniqueness of
the Nash equilibrium. Interestingly, although derived under stronger
constraints, incorporating for example spectral mask constraints, our
uniqueness conditions have broader validity than previously known conditions.
Finally, we assess the goodness of the proposed decentralized strategy by
comparing its performance with the performance of a Pareto-optimal centralized
scheme. To reach the Nash equilibria of the game, in Part II, we propose
alternative distributed algorithms, along with their convergence conditions.Comment: Paper submitted to IEEE Transactions on Signal Processing, September
22, 2005. Revised March 14, 2007. Accepted June 5, 2007. To be published on
IEEE Transactions on Signal Processing, 2007. To appear on IEEE Transactions
on Signal Processing, 200
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